Machine learning models based on temporal point processes arethe state ofthe artinawide variety ofapplications involving discrete events incontinuous time.

Inemergent communication studies, one often considers agents who canshare information about a set of objects described by the common features.

The substitution effect happens when learning the stronger features insome neurons can substitute for learning these features in other neurons through updating the predictionhead.

Hence, todefend against thebroad attack methods, we propose a regularized adversarial training scheme with low computational overheads.